Fracture risk assessment in early-stage autosomal dominant polycystic kidney disease: a cross-sectional study using FRAX^®, QFracture^®, and Garvan models

Abstract

Background

Compared with individuals with other chronic kidney disease (CKD), individuals with autosomal dominant polycystic kidney disease (ADPKD) may face increased fracture risk. This study provides the first clinical evaluation of fracture risk prediction in early-stage ADPKD using the FRAX^®, QFracture^®, and Garvan models.

Methods

In this cross-sectional study, adult ADPKD patients with preserved kidney function were screened for fracture risk. Due to age eligibility criteria and data availability, fracture risk estimates could be calculated in 67 individuals using QFracture^®, in 46 using FRAX^®, and in a subgroup of 23 participants using the Garvan calculator. Bone mineral density (BMD) was assessed via DXA, and height-adjusted total kidney volume (HtTKV) was assessed via MRI. Ten-year fracture risks were calculated and compared via correlation, Bland–Altman plots, and regression modeling.

Results

All evaluated calculators were broadly in agreement at the group level, and the median fracture risk values for major osteoporotic and hip fractures remained consistently low. In our cohort, QFracture^® appeared most practically applicable because of its broader age range and inclusion of CKD-related variables. Age was the only independent predictor of fracture risk; neither eGFR nor HtTKV was associated with fracture risk in any model. Preserved BMD suggests that DXA-derived measures alone may not fully capture skeletal vulnerability in ADPKD.

Conclusions

This is the first study to explore the use of online fracture risk calculators in an early-stage ADPKD population. Although fracture risk calculators offer a practical and cost-effective approach for estimating fracture risk, their use in ADPKD requires cautious interpretation. Our findings indicate that age significantly influences the predicted risk, whereas neither the eGFR nor HtTKV, despite their relevance to kidney disease progression, were associated with any of the evaluated models. Future research should aim to refine fracture risk models by incorporating disease-specific variables and validating them in larger, prospective ADPKD cohorts.

Introduction

Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary condition characterized by the progressive development of kidney cysts. It represents the fourth leading cause of kidney failure (KF) and affects approximately 12 million individuals worldwide [1, 2]. This disorder imposes substantial economic and social burdens, including increased healthcare costs, premature productivity losses, and significant emotional stress for affected individuals and their families [3].

Individuals with ADPKD may face a greater fracture risk than not only the general population but also those with other causes of chronic kidney disease (CKD) [4]. Disturbances in bone metabolism may already be present in early ADPKD stages [5]. However, predictive tools specifically tailored to assess fracture risk in ADPKD patients are currently lacking. Such tools should consider general population risks associated with ageing and CKD-related secondary hyperparathyroidism, along with aspects of the bone phenotype unique to ADPKD [6].

Notably, individuals with ADPKD are often younger and healthier than the broader CKD population, making the application of osteoporosis-focused fracture risk tools potentially inappropriate for this group. Moreover, skeletal fragility in ADPKD may not fully overlap with the pathophysiology of primary osteoporosis, which raises uncertainty regarding the performance and clinical applicability of osteoporosis-based fracture prediction tools in this population. Nevertheless, early recognition and increased awareness of fracture risk are crucial, as timely intervention and effective monitoring are essential in preserving public health and optimizing resource allocation. This is particularly significant given that fractures in CKD patients are associated with a markedly higher mortality rate, up to threefold greater than in non-CKD populations [7].

Addressing this challenge necessitates the development and validation of practical and scalable assessment tools. Online fracture risk assessment tools, such as FRAX^®, QFracture^®, and Garvan, offer promising solutions by integrating clinical history, lifestyle factors, genetic predispositions, environmental factors, and optional bone mineral density (BMD) measurements. Although these tools vary in their input variables and computational methodologies, their primary advantage lies in providing personalized fracture risk assessments and enabling earlier intervention.

The accessibility and user-friendliness of these calculators increase their potential utility, particularly for identifying at-risk individuals who might otherwise remain undiagnosed. This is especially relevant for ADPKD patients, in whom specialized diagnostic procedures, such as dual-energy X-ray absorptiometry (DXA) or bone biopsy, may not always be feasible.

The objective of this study was to compare the outcomes, feasibility and practical applicability of three online fracture risk prediction tools: FRAX^®, QFracture^®, and Garvan, in individuals with ADPKD in the early stages of CKD.

Materials and methods

Design and study population

This cross-sectional study included participants from a project examining the bone phenotype of ADPKD patients with preserved kidney function [8]. Only adult participants were recruited for the study. Individuals with diabetes mellitus, neoplasms, or inflammatory conditions as comorbidities were excluded. ADPKD was clinically diagnosed based on a positive family history and the ultrasound criteria of Ravine-Pei [9]. Additional inclusion criteria included the availability of relevant data on clinical risk factors for osteoporosis and fractures. Other major bone diseases constituted the exclusion criteria. As shown in Figs. 1 and 80 individuals were initially recruited for the study. However, eligibility for fracture risk assessment was determined using age thresholds specified by the respective tools: over 33 years for QFracture^®, over 40 years for FRAX^®, and over 50 years for the Garvan calculator. The initial eligible cohort, therefore, consisted of 67 participants who met the QFracture^® age criterion. Among them, 46 were also eligible for FRAX^® based on the age requirement; two additional participants were excluded from FRAX^® and Garvan analyses due to uninterpretable DXA scans, although they remained eligible for QFracture^®. A further restricted subgroup of 23 participants met the full input requirements for all three tools, including the Garvan calculator. Consequently, comparative analyses were performed within progressively smaller, age‑eligible subgroups to ensure methodological consistency.

Fig. 1.

Flow diagram of the study participant selection process

Clinical data and laboratory parameters

Relevant demographics, lifestyle information, comorbidities, medications and fracture history were collected during the recruitment interview, together with physical measurements. The estimated glomerular filtration rate (eGFR) was assessed in all patients via the Chronic Kidney Disease Epidemiology Collaboration formula [10].

Assessment of total kidney volume

The height-adjusted total kidney volume (HtTKV) was assessed via abdominal magnetic resonance imaging (MRI) according to the Mayo Imaging Classification (MIC) criteria [11].

Assessment of bone mass

BMD was measured via DXA via a Hologic Discovery densitometer Hologic QDR-4500 A (Hologic Inc., Marlborough, MA, USA). All scans were performed and interpreted in accordance with the International Society of Clinical Densitometry guidelines [12]. The results are expressed as absolute BMD (g/cm²). DXA images were subjected to trabecular bone score (TBS) measurements [iNsight software version 3.0.2 (Medimaps, Merignac, France)].

Fracture risk assessment

Fracture probabilities over a 10-year timeframe were manually calculated via three widely recognized tools: FRAX^®, QFracture^®, and the Garvan Fracture Risk Calculator. Calculations were performed via official online algorithms available at https://www.sheffield.ac.uk/FRAX, https://qfracture.org/, and https://www.garvan.org.au/bone-fracture-risk. Because the algorithms of the fracture‑risk calculators are not publicly available, all scores were obtained through manual entry. To ensure accuracy, each calculation was performed twice at two separate time points by the same trained researcher. The resulting dataset was subsequently checked for internal inconsistencies in the statistical software.

For FRAX^®, probabilities were estimated separately based on BMD derived from Hologic DXA and femoral neck T scores (n = 46). Additionally, TBS-adjusted FRAX^® values were calculated where TBS data were available (n = 46). Risk estimates were obtained for 46 participants via FRAX^®, 67 via QFracture^®, and 23 via Garvan.

The current analysis focuses on the subgroup of patients for whom all FRAX^® modalities—including BMD-adjusted and TBS-adjusted FRAX^®—could be applied, in comparison with QFracture^® (n = 46) or to both QFracture^® and Garvan (n = 23).

Definition of significant fracture risk

The definition of significant fracture risk was based on threshold values recommended by each tool’s developers, validation studies, or widely adopted clinical guidelines. Specifically:

FRAX^®: A 10-year probability of ≥ 20% for major osteoporotic fractures (MOFs) or ≥ 3% for hip fractures is widely recognized as an intervention threshold in clinical practice and was originally adopted in the National Osteoporosis Foundation (NOF) recommendations based on cost-effectiveness analyses. However, the setting of intervention thresholds needs to be country-specific. In Poland, only one study has evaluated such thresholds, and it was conducted in a hemodialysis population; therefore, its findings cannot be directly applied for comparison with the present study involving non-hemodialysis patients [13].

QFracture^®: A significant 10-year fracture risk was defined as ≥ 11.1% for women and ≥ 2.6% for men. These sex-specific thresholds were derived from external validation studies of QFracture scores in large UK primary care cohorts and correspond to risk levels associated with clinically meaningful discrimination and calibration performance for predicting osteoporotic and hip fractures [14]. As QFracture^® does not provide formal intervention thresholds, these empirically validated cutoffs represent the most widely accepted and methodologically justified approach for defining elevated fracture risk.

Garvan: Thresholds included a 10-year hip fracture risk between 3% and 9% and a 10-year risk for any osteoporotic fracture between 14% and 26%. These cut-offs were based on recommendations accompanying the Garvan Fracture Risk Calculator, where risks above the upper thresholds are considered to justify treatment initiation, while intermediate-risk values require individualized clinical assessment [15, 16].

Statistical analysis

All statistical analyses were performed using STATA software (version 18.0 BE, StataCorp LLC, Texas, USA), with a two‑tailed significance level set at p < 0.05. Descriptive statistics for each fracture risk prediction tool (FRAX^®, QFracture^®, and Garvan) are presented as medians with interquartile ranges (IQRs; Q1–Q3). The Shapiro–Wilk test was used to assess normality. Because the distributions of the variables were non‑normal, nonparametric methods were applied for group comparisons and correlation analyses. Associations between fracture risk estimates from the different tools were evaluated using Spearman’s rank correlation coefficient. Bland–Altman analyses were performed for pairwise comparisons, and 95% limits of agreement (LOA) were calculated.

Fracture risk scores were treated as continuous variables. Therefore, linear regression models were used to examine associations between clinical variables and 10‑year fracture risk. FRAX^® 10‑year MOF risk was modelled as a proportion (0–1), and regression coefficients are expressed as absolute changes in predicted risk (percentage points). A multivariable linear regression model was constructed to evaluate predictors of FRAX^®‑estimated risk, including age, body mass index (BMI), waist‑to‑hip ratio (WHR), serum creatinine, and eGFR as independent variables.

Results

Clinical data, fracture risk factors, BMD and TBS

The demographic and clinical characteristics of the patients in the study group are presented in Table 1.

Table 1.

Demographic and clinical characteristics of study participants with sufficient data to calculate fracture risk according to Garvan, FRAX^®, and QFracture^® expressed as median [IQR] for continuous variables and as counts (percentages) for categorical variables

Variable	Participants eligible for Garvan N = 23	Participants eligible for FRAX® N = 46	Participants eligible for QFracture® N = 67
Age (years)	54 [51–65]	49 [46–54]	47 [37–51]
Sex (M/F)	16/7	30/16	42/25
Creatinine [mg/dL]	1.52 [1.31–2.04]	1.48 [1.08–1.90]	1.39 [1.01–1.84]
Estimated GFR [ml/min/1.73 m2]	43 [32–59]	50 [33–72]	57 [40–86]
BMI (kg/m2)	25.4 [24.0-28.7]	25.9 [24.0-28.9]	26.0 [23.6–29.0]
WHR	0.91 [0.86–0.95]	0.89 [0.86–0.95]	0.89 [0.83–0.95]
SBP/DBP (mmHg)	150 [116–144]/ 87 [76–90]	127 [119–137]/85 [80–92]	126 [118–138]/86 [81–93]
htTKV (ml/m)	1348 [1002–2431]	1387 [853–2271]	1239 [808–2360]
BMD femoral neck (g/cm2)	0.78 [0.71–0.91]	0.80 [0.73–0.92]	0.84 [0.76–0.95]
T-score femoral neck	-0.95 [-1.64-0.11]	-0.65 [-1.37-0.24]	-0.52 [-1.11-0.51]
T-score lumbar spine	-0.17 [-1.1-0.48]	-0.12 [-1.09-0.62]	-0.11 [-1.04-0.65]
Trabecular bone score	1.36 [1.25–1.45]	1.37 [ 1.28–1.46]	1.38 [1.31–1.47]

BMD-Bone mineral density; BMI- Body mass index; htTKV- height-adjusted total kidney volume; GFR-glomerular filtration rate; WHR- waist-to-hip-ratio

A T-score indicative of osteoporosis was observed in one participant, whereas 14 met the diagnostic criteria for osteopenia. The prevalence of fracture risk factors, along with the variables required for risk assessment calculations, was comprehensively documented during the recruitment interviews and is summarized in Table 2.

Table 2.

Clinical risk factors and input variables used in fracture risk calculations

Variable	Garvan	FRAX®	QFracture®
No smoker/ex-smoker/active smoker	17/6/3	32/10/4	44/14/9
Smoker (light/moderate/heavy)	1/2/0	2/2/0	2/6/1
Alcohol consumption (3 or more units per day)	2	3	4
Prior fracture	2	2	3
Parental hip fracture	3	7	12
Recent falls	0	0	0
Secondary osteoporosis	1	1	1
Dementia	0	0	0
Cancer	0	0	0
Asthma or COPD	0	0	0
Cardiovascular disease	6	6	6
Chronic liver disease	0	0	0
Rheumatoid arthritis or SLE	1	1	1
Hyperparathyroidism	7	11	16
Malabsorption	0	0	0
Epilepsy/anticonvulsants	0	0	0
Glucocorticoids	0	2	3
Bisphosphonates	0	0	0
HRT (estrogen only)	0	0	0
Antidepressants	1	1	4

Results of the 10-year fracture probability

Fracture risk estimates derived from all online calculations exhibited a nonnormal distribution pattern. Regardless of the assessment tool used, the median risk values for MOF and hip fractures remained consistently low across the final analyzed cohort (n = 46). Table 3 presents the 10-year fracture probabilities, expressed as median values with interquartile ranges (Q1–Q3). The 10-year fracture risk calculated via the QFracture^® was similarly low in the overall study population (n = 67), with a median MOF risk of 1.4% [0.9–2.1%] and a hip fracture risk of 0.2% [0.1–0.4%].

Table 3.

Results of 10-year fracture risk in participants with complete data for FRAX^® and QFracture^® calculations [A.] and in the subgroup eligible for Garvan calculations (aged ≥ 50 years) [B.]

Prediction tool	Calculated risk median [Q1-Q3] %
	MOF/other (N = 46)	Hip fracture (N = 46)
FRAX® BMD	1.95 [1.70–2.70]	0.10 [0.00-0.40]
FRAX® (TBS + BMD)	1.90 [1.00-2.90]	0.10 [0.00-0.30]
FRAX® T score	2.20 [1.75–3.15]	0.20 [0.05–0.50]
FRAX®(TBS + Tscore)	2.05 [0.93–3.18]	0.10 [ 0.00-0.48]
QFracture®	1.65 [0.98–2.33]	0.30 [0.10–0.60]

Prediction tool	Calculated risk median [Q1-Q3] %
	MOF/other (N = 23)	Hip fracture (N = 23)
FRAX® BMD	2.50 [1.90–2.80]	0.30 [0.10–0.70]
FRAX® (TBS + BMD)	2.55 [1.38–3.65]	0.25 [0-0.68]
FRAX® T score	2.60 [ 2.05–3.45]	0.50 [0.08–1.13]
FRAX®(TBS + Tscore)	2.70 [1.00-4.10]	0.40 [ 0.10-1.00]
QFracture®	2.00 [1.20–3.10]	0.60 [0.30–1.20]
Garvan	4.00 [2.00–8.00]	0.30 [0.10-1.00]

Abbreviations: BMD - Bone mineral density; MOF - major osteoporotic fracture; NA – not applicable; TBS - trabecular bone score

When assessed individually, a significant fracture risk, exceeding the established threshold values, was identified in one participant for MOF and nine for hip fracture, using FRAX^®; while in six participants, according to QFracture^®.

Concordance of fracture risk across different prediction tools

Univariate analysis revealed positive, but statistically nonsignificant, associations between risks calculated according to FRAX^® and QFracture^® (N = 46). The best concordance was present between the FRAX^® and Garvan tools in the 10-year prediction of hip fracture (R = 0.873; P < 0.05; N = 23).

Bland–Altman analysis revealed no systematic over- or underestimation of the 10-year MOF probability between FRAX^® and QFracture^®, with a mean difference 0.37% points (pp) (95% LoA − 2.1pp to 2.8 pp). Similarly, for the 10-year hip fracture risk, the mean difference was − 0.2 pp, with 95% LoA from − 1.3 to 1.0% points (Fig. 2).

Fig. 2.

Bland‒Altman plots showing agreement between FRAX^® and QFracture results for fracture risk assessment of major osteoporotic fracture (A) and hip fracture (B). The X-axis represents the average fracture risk obtained via both methods, whereas the Y-axis represents the difference between the two methods. The solid line represents the mean difference (bias), and the dashed lines represent the 95% limits of agreement. Bland–Altman differences are expressed in fractional units (proportions), which correspond to percentage‑point differences when multiplied by 100

For 10-year hip fracture risk, Bland–Altman plots demonstrated agreement across all three calculators. FRAX^® and Garvan differed by only − 0.07 pp (95% LOA–0.09pp to-0.8 pp), while QFracture^® and Garvan by mean difference of − 0.2 pp; 95% LOA − 1.6 to 1.1 pp although statistical concordance was limited by the small sample size.

Kidney volume and other predictors of fracture risk

HtTKV did not show a significant association with 10-year fracture risk probabilities in univariate models (e.g., FRAX^® MOF: β = 0.002, P = 0.645; QFracture^® hip: β = 0.0005, P = 0.369). These findings remained unchanged after adjustment for age, eGFR and BMI.

Across all the calculators, age was a core predictor of increasing fracture risk. A multivariable linear regression model was constructed to evaluate predictors of 10-year MOF risk as estimated by FRAX^®. The model included age, BMI, WHR, serum creatinine, and eGFR as independent variables. The overall model was statistically significant (F(5, 40) = 4.11; p = 0.004), explaining approximately 34% of the variance in FRAX^®-estimated risk (R² = 0.339; adjusted R² = 0.256). Among the included variables, age was the only statistically significant predictor, with each additional year associated with a 0.13% increase in 10-year fracture risk (β = 0.00125; 95% CI: 0.00039–0.00211; P = 0.006). Other variables, BMI, WHR, creatinine, and eGFR, did not show significant associations with the FRAX^® score. Effect sizes were small and not clinically meaningful, consistent with the narrow range of fracture probabilities in this cohort.

Discussion

To our knowledge, this is the first study to explore the use of online fracture risk calculators, specifically in the ADPKD population. Our analyses compared the predicted fracture‑risk values, not the predictive performance or clinical accuracy of the calculators. We deliberately chose internet-based tools because of their accessibility and ease of use in clinical settings. While the formal applicability of these tools was limited, primarily due to the younger age of our cohort (Fig. 1), at least one calculator (QFracture^®) could be applied in 67 out of 80 participants (83%). However, a direct comparison of risk estimates generated by FRAX^® and QFracture^® in the same individual was feasible in only 46 cases, and a comparison of all three calculators, including Garvan, was possible in just 23 participants.

The calculators yielded broadly comparable estimates of 10-year fracture risk across all analyzed tools. However, the small sample size, cross-sectional design, and lack of prospective fracture outcome data, limit the ability to assess true fracture prediction performance. Although the calculated 10-year fracture risk was generally low, fracture risk may increase substantially as ADPKD progresses and kidney function deteriorates, particularly in patients who develop advanced CKD or require kidney replacement therapy. As previously reported by Ziolkowski et al. [4]., a significant number of patients experience fractures in the second year after the initiation of dialysis. A subset of our cohort is likely to meet the criteria for rapid progression of ADPKD [11] and will require kidney replacement therapy within the next decade. This aspect may not be adequately captured by currently available fracture risk calculators, particularly in the context of systemic bone involvement, which is increasingly recognized as part of the ADPKD phenotype [8].

On the basis of the DXA scans, BMD was also not reduced in the study group. The elevated fracture risk in this population appears to be independent of the presence of osteoporosis as diagnosed by DXA. This finding is consistent with current clinical guidelines, which advise against routine DXA assessment in individuals with CKD under the age of 50 [12, 17]. Consequently, the use of fracture risk assessment tools such as FRAX^® and Garvan becomes problematic, as both incorporate DXA-derived parameters in their algorithms. Alternative methods for fracture risk estimation should therefore be considered, particularly for younger patients and those with early-stage CKD (stages G1–2).

FRAX^® is the most widely used tool among those we analyzed. In 2008, the World Health Organization marked a significant milestone by introducing FRAX^® as a method for identifying candidates for pharmacological treatment of osteoporosis [18]. While FRAX^® has demonstrated reliability in predicting MOF among patients with CKD stages G3a–G3b, its inability to include CKD as a specific risk factor and its reduced applicability in advanced CKD (stages G4-G5D) remain substantial limitations [13, 19, 20].

Among the analyzed calculators, QFracture^® presented the broadest inclusion criteria. This British-developed calculator incorporates factors such as age, sex, BMI, race, and other additional clinical risk variables to estimate fracture risk [21]. In this clinical context, QFracture offers several advantages. It accounts for CKD, albeit limited to advanced stages, incorporates hyperparathyroidism as a risk factor, and does not rely on the availability of DXA scans. Moreover, its broader age range (33–90 years) makes it particularly suitable for younger individuals with CKD, such as those with ADPKD.

The Garvan Fracture Risk Calculator, developed by the Garvan Institute of Medical Research, is designed primarily to predict osteoporotic and hip fractures in individuals over the age of 50. A distinguishing feature of the Garvan tool is its inclusion of fall history, a critical risk factor for fractures that is not considered in FRAX^®. This is particularly relevant in dialysis populations, where the incidence of accidental falls may be increased. However, the relationship between a declining GFR and fall risk remains controversial [22]. Notably, the Garvan tool has undergone extensive validation in large cohorts, including individuals with chronic kidney disease (CKD). Despite its simplicity, the Garvan tool demonstrated reduced discriminatory capacity for CKD patients with stage G3 and an underestimation of fracture risk in this group [23]. Compared with FRAX^®, Garvan has greater sensitivity in identifying individuals who have sustained fractures, whereas FRAX^® has greater specificity for those who have not. Interestingly, even within our modest study cohort, the Garvan tool appeared to underestimate fracture risk compared with the FRAX^®. This observation aligns with prior research involving over 9,000 participants with CKD stages G2 and G3 [23].

The ultimate choice of a tool may be guided by clinical context and data availability. Among all tools, QFracture^® appeared most practically applicable in our cohort.

Our analysis revealed that age was a significant determinant of fracture risk across all tools, whereas neither eGFR nor HtTKV, a recognized marker of disease progression in ADPKD, were associated with fracture risk scores. This finding highlights a disconnect between the factors driving kidney disease progression and those captured by current fracture risk calculators. Given that kidney volume is not included in these tools, its lack of association with fracture risk is not unexpected. The systemic factors could contribute to bone fragility in ADPKD. However, the low and narrowly distributed fracture risk estimates, together with the limited sample size, reduce the statistical power of the analyses and may restrict the clinical interpretability of the regression coefficients.

Our study has several strengths. To our knowledge, this is the first attempt to address the issue of increased fracture risk in patients with ADPKD in a clinical context. It provides real-world data from a well-characterized cohort of individuals across the spectrum of earlier CKD stages. A key methodological strength is that all participants underwent DXA as part of their bone phenotyping process. Moreover, all data entered into the fracture risk calculators were self-reported by patients and carefully curated before the manual calculation of the individual results. The study simultaneously applied and compared multiple validated fracture risk calculators (FRAX^®, Garvan, QFracture^®), allowing for a nuanced evaluation of their applicability in the ADPKD population.

Nevertheless, this study has several limitations. First, it includes a small sample size. Second, owing to its cross-sectional design, it was not possible to validate the predicted fracture risk against actual fracture occurrences. A prospective validation would require a substantially larger cohort and long-term follow-up, which poses considerable challenges given the prevalence of ADPKD. Significant progress in this area is anticipated through the development of national and international ADPKD registries, which may facilitate the monitoring of health outcomes, including fractures, and support future recalibration of existing risk models—an adjustment that appears increasingly inevitable. Collaborative, multicenter efforts are needed not only to generate robust data but also to integrate fracture risk assessment into everyday clinical practice for patients with ADPKD.

In conclusion, fracture risk calculators offer a practical and cost-effective approach to estimating fracture risk, but their use in ADPKD requires cautious interpretation. Among the tools yielding concordant results, QFracture^® appeared most practically applicable, particularly because of its broader age range and inclusion of CKD and hyperparathyroidism. Although these calculators primarily assess osteoporosis-related fractures, their application in ADPKD may help identify high-risk individuals. Future research should aim to refine fracture risk models by incorporating disease-specific variables and validating them in larger, prospective ADPKD cohorts.

Author contributions

HK calculated fracture risk, analyzed the results and contributed to the writing of the manuscript; PM interpreted the data and contributed to the writing of the manuscript; MH critically analyzed the results; MDZ interviewed patients and gathered the dataset; IK-G interpreted the data and performed the statistical analysis; ADS interpreted the patient’s data and mentored the team; DM critically analyzed the results and contributed to the writing of the manuscript; MJ designed and overviewed the study, obtained funding, and was a major contributor to the writing of the manuscript. All the authors read and approved the final manuscript.

Funding

This research was funded by a grant from the National Science Centre, Kraków, Poland (No. 2018/30/M/NZ5/00480). This publication was funded by the Medical University of Gdańsk, Skłodowskiej-Curie St. 3a, 80-210 Gdańsk, project number 01-10026, task 0010407. DM is supported by the Research Foundation Flanders (FWO) under grants G0C8920N and G060623N and the clinical senior research grant 1704123 N.

Data availability

The data that support the findings of this study are available from the Medical University of Gdańsk, but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the Medical University of Gdańsk.

Declarations

Ethics approval and consent to participate

The study adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Committee at the Medical University of Gdańsk (No. NKBBN/335/2017). All participants provided written informed consent before enrollment. Ethics approval and consent to participate.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.